1. Data Preparation & Exploration

This phase involves cleaning and preparing the data for analysis.

Techniques

• Handling Missing Data: Use imputation techniques for missing values in purchase history, product details, etc.
• Data Normalization: Standardize or scale numerical attributes like purchase amount.
• Feature Engineering: Create new features like Average Purchase Value, Recency (last purchase), Frequency of Orders, etc.

Visualizations

• Missing Data Heatmap (Seaborn’s heatmap): Identify missing values in key fields.
• Histograms (Matplotlib/Seaborn): Understand distributions of numerical features like order value or customer age.

2. Customer Behavior Analysis

Understanding how customers interact with the platform.

Techniques

• Recency, Frequency, and Monetary (RFM) Analysis: Segment customers based on how recently and frequently they purchased.
• Customer Segmentation using Clustering (K-Means, Hierarchical Clustering): Identify groups based on behavior.
• Cohort Analysis: Track user retention over time.

Visualizations

• RFM Distribution Plot (Boxplot/Histogram): Identify high-value customers.
• Churn Rate Trend (Line chart): Show customers who have stopped purchasing over time.
• Customer Journey Flowchart (Sankey Diagram): Visualize user pathways on the platform.